47 citations · 82 across the 11 of their papers we have counts for
13 papers
Active Learning of Piecewise Gaussian Process Surrogates
Chiwoo Park, Robert Waelder, Bonggwon Kang +3
Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisit…
Gaussian Process Model for Estimating Piecewise Continuous Regression Functions
Chiwoo Park
This paper presents a Gaussian process (GP) model for estimating piecewise continuous regression functions. In scientific and engineering applications of regression analysis, the u…
Data Science for Motion and Time Analysis with Modern Motion Sensor Data
Chiwoo Park, Sang Do Noh, Anuj Srivastava
The motion-and-time analysis has been a popular research topic in operations research, especially for analyzing work performances in manufacturing and service operations. It is reg…
Variable selection for Gaussian process regression through a sparse projection
Chiwoo Park, David J. Borth, Nicholas S. Wilson +1
This paper presents a new variable selection approach integrated with Gaussian process (GP) regression. We consider a sparse projection of input variables and a general stationary…
Robust Gaussian Process Regression with a Bias Model
Chiwoo Park, David J. Borth, Nicholas S. Wilson +2
This paper presents a new approach to a robust Gaussian process (GP) regression. Most existing approaches replace an outlier-prone Gaussian likelihood with a non-Gaussian likelihoo…
Gaussian process surrogate modeling with manipulating factors for carbon nanotube growth experiments
Chiwoo Park, Rahul Rao, Pavel Nikolaev +1
This paper presents a new Gaussian process (GP) surrogate modeling for predicting the outcome of a physical experiment where some experimental inputs are controlled by other manipu…
Sequential Adaptive Design for Jump Regression Estimation
Chiwoo Park, Peihua Qiu, Jennifer Carpena-Núñez +3
Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this pape…
Patchwork Kriging for Large-scale Gaussian Process Regression
Chiwoo Park, Daniel Apley
This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regio…
Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
Xin Li, Alex Belianinov, Ondrej Dyck +2
This paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microsc…
A Mixture of Linear-Linear Regression Models for Linear-Circular Regression
Ali Esmaieeli Sikaroudi, Chiwoo Park
We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modeling approach of a wrapped norma…
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background
Garret Vo, Chiwoo Park
This paper presents a robust regression approach for image binarization under significant background variations and observation noises. The work is motivated by the need of identif…
Sparse Filtered SIRT for Electron Tomography
Chen Mu, Chiwoo Park
Electron tomographic reconstruction is a method for obtaining a three-dimensional image of a specimen with a series of two dimensional microscope images taken from different viewin…
Directional Statistics of Preferential Orientations of Two Shapes in Their Aggregate and Its Application to Study Preferential Attachment of Nanoparticles
Ali Esmaieeli Sikaroudi, David A. Welch, Taylor Woehl +4
Nanoscientists have long conjectured that adjacent nanoparticles aggregate with one another in certain preferential directions during a chemical synthesis of nanoparticles, which i…